scholarly journals Studi Penambatan Molekuler Senyawa Bioaktif Biji Habbatussauda (Nigella sativa) terhadap ERα sebagai Alternatif Pengobatan Kanker Payudara dalam Upaya Pemberian Data Ilmiah Thibbun Nabawi

2021 ◽  
Vol 8 (1) ◽  
pp. 13-24
Author(s):  
Vega Mylanda ◽  
Norman Emil Ramadhan ◽  
Rafiqah Nur Viviani
Keyword(s):  
Genetic Algorithm ◽  
Nigella Sativa ◽  
Lamarckian Genetic Algorithm ◽  
Autodock 4.2

Pendahuluan: Reseptor Estrogen α (ERα) merupakan salah satu target reseptor utama dari pengobatan kanker payudara, sehingga penghambat ERα adalah salah satu obat yang paling potensial dalam pengobatan kanker payudara. Pencarian terhadap molekul penghambat ERα dapat ditemukan pada senyawa dari tanaman tradisional, seperti misalnya habbatussauda (Nigella sativa). Habbatussauda telah dijelaskan pada Thibbun Nabawi sebagai tumbuhan yang dapat mengobati segala penyakit, namun masih belum ada penelitian yang menjelaskan senyawa dalam habbatussauda sebagai penghambat ERα. Habbatussauda diketahui memiliki beberapa kandungan senyawa yang memiliki aktivitas farmakologis, seperti antioksidan dan antikanker. Penelitian ini bertujuan untuk mengetahui potensi dan interaksi dari senyawa yang terkandung dalam habbatussauda sebagai pengobatan baru kanker payudara dengan target ERα. Metode: Penelitian ini menggunakan metode penambatan molekuler dengan peranti lunak AutoDock 4.2 dengan metode pencarian Lamarckian Genetic Algorithm (LGA). Penambatan molekuler dikenal ilmuwan sebagai metode yang cepat dan hemat biaya. Hasil: Hasil penelitian ini menunjukan bahwa senyawa stigmasterol dalam habbatussauda berpotensi sebagai inhibitor ERα dengan nilai ΔG sebesar -10,14 kkal/mol dan Ki sebesar 36,99 nM. Kesimpulan: Penelitian ini menunjukkan bahwa stigmasterol merupakan kandidat potensial sebagai inhibitor ERα yang baru. Selain itu, penelitian ini mendapatkan beberapa residu asam amino yang diduga penting dalam aktivitas penghambatan ERα, yaitu Leu346, Glu353, Leu387, dan Arg394, serta memberikan bukti ilmiah bahwa senyawa kimia dalam Nigella sativa memiliki potensi sebagai obat kanker payudara.

scholarly journals Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

1998 ◽  
Vol 19 (14) ◽  
pp. 1639-1662 ◽  
Author(s):  
Garrett M. Morris ◽  
David S. Goodsell ◽  
Robert S. Halliday ◽  
Ruth Huey ◽  
William E. Hart ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Free Energy ◽  
Energy Function ◽  
Binding Free Energy ◽  
Free Energy Function ◽  
Lamarckian Genetic Algorithm ◽  
Automated Docking

A new Lamarckian genetic algorithm for flexible ligand-receptor docking

2010 ◽  
pp. NA-NA ◽  
Author(s):  
Jan Fuhrmann ◽  
Alexander Rurainski ◽  
Hans-Peter Lenhof ◽  
Dirk Neumann
Keyword(s):  
Genetic Algorithm ◽  
Flexible Ligand ◽  
Lamarckian Genetic Algorithm

scholarly journals Potential Inhibitor of COVID-19 Main Protease (Mpro) From Several Medicinal Plant Compounds by Molecular Docking Study

2020 ◽  
Author(s):  
Siti Khaerunnisa ◽  
Hendra Kurniawan ◽  
Rizki Awaluddin ◽  
Suhartati Suhartati ◽  
Soetjipto Soetjipto
Keyword(s):  
Molecular Docking ◽  
Treatment Options ◽  
Docking Study ◽  
Binding Energies ◽  
Molecular Docking Study ◽  
Epicatechin Gallate ◽  
Discovery Studio ◽  
Main Protease ◽  
Lamarckian Genetic Algorithm ◽  
Autodock 4.2

COVID-19, a new strain of coronavirus (CoV), was identified in Wuhan, China, in 2019. No specific therapies are available and investigations regarding COVID-19 treatment are lacking. Liu et al. (2020) successfully crystallised the COVID-19 main protease (Mpro), which is a potential drug target. The present study aimed to assess bioactive compounds found in medicinal plants as potential COVID-19 Mpro inhibitors, using a molecular docking study. Molecular docking was performed using Autodock 4.2, with the Lamarckian Genetic Algorithm, to analyse the probability of docking. COVID-19 Mpro was docked with several compounds, and docking was analysed by Autodock 4.2, Pymol version 1.7.4.5 Edu, and Biovia Discovery Studio 4.5. Nelfinavir and lopinavir were used as standards for comparison. The binding energies obtained from the docking of 6LU7 with native ligand, nelfinavir, lopinavir, kaempferol, quercetin, luteolin-7-glucoside, demethoxycurcumin, naringenin, apigenin-7-glucoside, oleuropein, curcumin, catechin, epicatechin-gallate, zingerol, gingerol, and allicin were -8.37, -10.72, -9.41, -8.58, -8.47, -8.17, -7.99, -7.89, -7.83, -7.31, -7.05, -7.24, -6.67, -5.40, -5.38, and -4.03 kcal/mol, respectively. Therefore, nelfinavir and lopinavir may represent potential treatment options, and kaempferol, quercetin, luteolin-7-glucoside, demethoxycurcumin, naringenin, apigenin-7-glucoside, oleuropein, curcumin, catechin, and epicatechin-gallate appeared to have the best potential to act as COVID-19 Mpro inhibitors. However, further research is necessary to investigate their potential medicinal use.

scholarly journals A Hybrid DELGA Algorithm for Protein Ligand Docking

2020 ◽  
Vol 9 (4) ◽  
pp. 2836-2840
Keyword(s):  
Genetic Algorithm ◽  
Molecular Modeling ◽  
Binding Energy ◽  
Research Work ◽  
Ligand Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Lamarckian Genetic Algorithm ◽  
Computational Molecular Modeling ◽  
Docking Energy

Protein-ligand docking is a computational molecular modeling method that is used in drug design to predict the optimal binding pose between the ligand and receptor. AutoDock is an open-source freeware program used to predict docking poses. It uses LGA) Lamarckian genetic algorithm to enumerate the binding energy. In this research work, we proposed an approach of hybrid Differential evolution base Lamarckian genetic (DELGA) algorithm to calculate the lowest binding energy. The experiment conducted to compute the 65 molecular instances, the results exposed that our approach predicts lowest docking energy with minimum root mean square deviation (RMSD) in comparison to the LGA, SA and PSO algorithms.

A Lamarckian Genetic Algorithm Applied to the Travelling Salesman Problem

1999 ◽  
Vol 02 (04) ◽  
pp. 431-457 ◽  
Author(s):  
Bereket T. Tesfaldet ◽  
Augusto Y. Hermosilla
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Local Optimum ◽  
Performance Improvements ◽  
Mutation Operators ◽  
Lamarckian Genetic Algorithm

Genetic Algorithms (GAs) comprise a class of adaptive heuristic search methods analogous to genetic inheritance and Darwinian strife for survivial of individuals within a population. Today, GAs are widely used to solve complex optimization problems, including ill-conditioned and NP-complete types arising in business, commerce, engineering, large-scale industries, and many other areas. To address these wide areas of applications and to improve upon their drawbacks, many variations and modifications of GAs have been proposed. The GA variation proposed in this paper has four basic operators: reproduction, recombination and two mutation operators, particularly applied to the famous and extensively studied Traveling Salesman Problem (TSP) in large-scale combinatorial optimization. Three of the operators use diversity information (standard deviation of costs) from the current population to adjust the diversity of the next population. The fourth one is an introduced new mutation operator called p-displacement that simulates the Lamarckian evolutionary learning and training concepts of gene improvement to bring chromosomes to their local optimum. We call the proposed GA: Lamarckian Genetic Algorithm-Traveling Salesman Problem (LGA-TSP). Emprical results show performance improvements compared to the classic and other modified GAs, as well as simulated annealing.

scholarly journals DOCKING ANALOG KURKUMIN TURUNAN PIPERAZINDION DENGAN TUBULIN (1TUB) RANTAI  MENGGUNAKAN VINA DAN AUTODOCK1

2015 ◽  
Vol 12 (1) ◽  
pp. 14-18
Author(s):  
Broto Santoso
Keyword(s):  
Genetic Algorithm ◽  
Density Functional Theory ◽  
Monte Carlo ◽  
Simulated Annealing ◽  
Density Functional ◽  
Citrus Reticulata ◽  
Monte Carlo Data ◽  
Functional Theory ◽  
Lamarckian Genetic Algorithm

Program Autodock mampu memprediksi energi bebas dan konformasi ikatan antara fleksibel ligan dan makromolekul target yang telah diketahui. Senyawa turunan dan analog kurkumin adalah ligan yang telah banyak dihasilkan dan diuji aktivitasnya. Beberapa diantaranya memiliki khasiat yang lebih baik dari kurkumin. Enam senyawa turunan piperazindion, kurkumin, PGV-0, dan PGV-1 dihitung energi optimasi geometrinya menggunakan density functional theory (DFT) – Gaussian. Ligan hasil optimasi dicari energi ikatan ligan dengan reseptor 1TUB rantai b melalui docking menggunakan Vina dan Autodock dengan metode Lamarckian Genetic Algorithm (LGA), traditional Genetic Algorithm (tGA), dan Simulated Annealing (SA) Monte Carlo. Data energi ikatan (affinitas) terbaik yang diperoleh dianalisis dengan Anova: Two-Factor Without Replication (P=0,01). Hasil docking dengan semua metode menunjukkan bahwa senyawa analog kurkumin turunan piperazindion mempunyai potensi ikatan lebih baik dibanding senyawa induknya Kata Kunci: 1TUB, Autodock, docking, kurkumin, piperazindionage:IN'Kata kunci: Citrus reticulata, antiproliferatif, DMBA, AgNOR, c-Myc. 

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